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Titlebook: Data Mining and Knowledge Discovery via Logic-Based Methods; Theory, Algorithms, Evangelos Triantaphyllou Book 2010 Springer Science+Busin

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發(fā)表于 2025-3-21 18:30:53 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Data Mining and Knowledge Discovery via Logic-Based Methods
副標(biāo)題Theory, Algorithms,
編輯Evangelos Triantaphyllou
視頻videohttp://file.papertrans.cn/263/262935/262935.mp4
概述Using a novel method, the monograph studies a series of interconnected key data mining and knowledge discovery problems.Provides a unique perspective into the essence of some fundamental Data Mining i
叢書名稱Springer Optimization and Its Applications
圖書封面Titlebook: Data Mining and Knowledge Discovery via Logic-Based Methods; Theory, Algorithms,  Evangelos Triantaphyllou Book 2010 Springer Science+Busin
描述The importance of having ef cient and effective methods for data mining and kn- ledge discovery (DM&KD), to which the present book is devoted, grows every day and numerous such methods have been developed in recent decades. There exists a great variety of different settings for the main problem studied by data mining and knowledge discovery, and it seems that a very popular one is formulated in terms of binary attributes. In this setting, states of nature of the application area under consideration are described by Boolean vectors de ned on some attributes. That is, by data points de ned in the Boolean space of the attributes. It is postulated that there exists a partition of this space into two classes, which should be inferred as patterns on the attributes when only several data points are known, the so-called positive and negative training examples. The main problem in DM&KD is de ned as nding rules for recognizing (cl- sifying) new data points of unknown class, i. e. , deciding which of them are positive and which are negative. In other words, to infer the binary value of one more attribute, called the goal or class attribute. To solve this problem, some methods have been sugge
出版日期Book 2010
關(guān)鍵詞Artifical Intelligence; Boolean function; Data Analysis; Decision Making; Intelligent Systems; Knowledge
版次1
doihttps://doi.org/10.1007/978-1-4419-1630-3
isbn_softcover978-1-4614-2613-4
isbn_ebook978-1-4419-1630-3Series ISSN 1931-6828 Series E-ISSN 1931-6836
issn_series 1931-6828
copyrightSpringer Science+Business Media, LLC 2010
The information of publication is updating

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發(fā)表于 2025-3-21 23:03:57 | 只看該作者
A Revised Branch-and-Bound Approach for Inferring a Boolean Function from Examplesng examples. This algorithm is an extension of the B&B algorithm described in the previous chapter. Now the states of the search space are described by using more information and this seems to be critical in leading to good search results faster. This chapter is based on the developments first prese
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Some Fast Heuristics for Inferring a Boolean Function from Examplesor inferring a Boolean function in the form of a compact (i.e., with as few clauses as possible) CNF or DNF expression from two collections of disjoint examples. As was described in Chapters 2 and 3, the B&B approaches may take a long time to run (actually, they are of exponential time complexity).
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An Incremental Learning Algorithm for Inferring Boolean Functionsheoracle for classification and use that information to improve the understanding of the system under consideration. When the new example would unveil the need for an update, one had to use all the existing training examples, plus the newly classified example, to infer a new (and hopefully more accu
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The Reliability Issue in Data Mining: The Case of Computer-Aided Breast Cancer Diagnosisst data. This accuracy can be a general description of how well the extracted model classifies test data. Some studies split thisaccuracy rate into two rates: thefalse-positive andfalse-negative rates. This distinction might be more appropriate for most real-life applications. For instance, it is on
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Data Mining and Knowledge Discovery by Means of Monotone Boolean Functionsly inferred if all possible binary examples (states) in the space of the attributes are used for training. Thus, one may never be 100% certain about the validity of the inferred knowledge when the number of training examples is less than 2.. The situation is different, however, if one deals with the
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Some Application Issues of Monotone Boolean Functionsuracy make the search for this property in data and its consecutive algorithmic exploitation, to be of high potential in data mining and knowledge discovery applications. The following developments are based on the work described in [ Kovalerchuk,Vityaev, andTriantaphyllou, 1996] and [ Kovalerchuk,T
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